Operations | Monitoring | ITSM | DevOps | Cloud

Subsea cables have long been the invisible backbone of the internet, carrying more than 95% of global data traffic beneath the ocean’s surface. Today, they are no longer just background infrastructure, they sit at the centre of an increasingly complex digital and geopolitical landscape. The rise of artificial intelligence, alongside continued cloud expansion and hyperscale data centre growth, is driving unprecedented demand for high-capacity, low-latency connectivity.

CI/CD pipelines do four things: it builds code, runs tests against mocked dependencies, lints for style violations, and scans for known vulnerability patterns. What it cannot do is validate how that code behaves under real users, real service responses, and real runtime constraints that staging was never configured to reproduce. That entire class of failure clears every gate cleanly and surfaces only in production.

If you've read our previous piece on the 8 stages of AI engineering maturity, you know where your team sits. Turns out adopting AI is the easy part; adapting to its consequences is where most organizations struggle. For more than a decade, software organizations optimized around a single assumption: implementation capacity was scarce.

IT infrastructure automation sounds simple enough on the surface, right? You take repetitive infrastructure work, turn it into automated workflows, and give engineers more time for higher-value problems. This may seem easy, but in practice, it gets more interesting. Modern IT environments are spread across cloud platforms, legacy systems, identity tools, ITSM platforms, monitoring systems, network devices, and business-critical applications.

AI pricing covers the cost structures and billing models providers use to charge for AI products: per-token APIs (GPT-4o at $2.50/1M input tokens), per-seat subscriptions (Copilot at $30/user/month), per-conversation billing (Agentforce at $2/conversation), and consumption-based GPU compute (H100 instances at $55.04/hour). There is no standard. The total AI cost is almost always higher than the sticker price.

In Explorer, you build a filter set and group-by to answer a cost question, and often that’s exactly the configuration you’d want to save for later. But saving it as a View meant navigating away from Explorer, opening the Views page, and rebuilding the same configuration from scratch: filter by filter, dimension by dimension. That friction was enough to discourage saving exploratory analysis as a View at all You can now save any Explorer analysis as a View in place.